Chunk 50.0

This chunk focused on provisioning kpro6 for production training and iterating on the pipeline to balance throughput, power draw, and training correctness. A new LXC container (CT 200) was spun up with Ubuntu 24.04, 8× RTX PRO 6000 GPUs, and a complete Python environment (PyTorch 2.11, transformers 5.8, FLA, wandb). After fixing Triton compilation issues and OOM errors caused by logits computation on target and drafter GPUs (by skipping `lm_head` on targets and computing verifier logits only at anchor positions), the first stable training run was launched with a 7-1 GPU topology, achieving ~27 Ktok/s. To reduce rack power draw, the topology was switched to 6-1, saving ~1 kW while maintaining a balanced pipeline at ~30 Ktok/s and an ETA of ~4.2 days. The user identified a critical flaw in the training data pipeline: the `build_batches` function sorted all samples by length and created fixed batch assignments. While batch order was shuffled each epoch, the composition of samples within each batch remained static, meaning the optimizer always saw short samples together and long samples together. The user correctly flagged this could lead to gradient oscillation and poor convergence. A full random shuffle was tested to fix this, but it destroyed padding efficiency, dropping throughput to ~12 Ktok/s. The user and assistant then collaborated on a hybrid "bucketed shuffle" strategy to recover throughput while ensuring diverse batch compositions. To implement the bucketed shuffle optimally, an analytical optimization script was run against the actual sequence length distribution of the 902K samples. It determined the optimal 6 bucket boundaries `[0, 770, 1216, 1728, 2432, 3296, 8192]` to minimize padding waste, achieving an estimated ~87% efficiency. The training run was stopped to implement this new batching strategy. The chunk concludes with the assistant ready to modify the `build_batches` function to implement per-epoch bucketed shuffling, aiming to restore throughput close to the original ~30 Ktok/s while providing the gradient diversity needed for robust convergence over the full 6 epochs.

From Infrastructure to Insight: The DFlash Training Pipeline on kpro6 2177 words

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